It is becoming increasingly important and urgent to rapidly and accurately identify toxic materials or pathogens with a high degree of reliability, particularly when the toxins/pathogens may be purposefully or inadvertently mixed with other materials. In uncontrolled environments, such as the atmosphere, a wide variety of airborne organic particles from humans, plants and animals occur naturally. Many of these naturally occurring organic particles appear similar to some toxins and pathogens, even at a genetic level. It is important to be able to distinguish between these organic particles and the toxins/pathogens.
In cases where toxins and/or pathogens are purposely used to inflict harm or damage, they are typically mixed with so called “masking agents” to conceal their identity. These masking agents are used to trick various detection methods and apparatus to overlook or be unable to distinguish the toxins/pathogens mixed therewith. This is a recurring concern for homeland security where the malicious use of toxins and/or infectious pathogens may disrupt the nation's air, water and/or food supplies. Additionally, certain businesses and industries could also benefit from the rapid and accurate identification of the components of mixtures and materials. One such industry that comes to mind is the drug manufacturing industry, where the identification of mixture composition could aid in preventing the alteration of prescription and non-prescription drugs.
One known method for identifying materials and organic substances contained within a mixture is to measure the absorbance, transmission, reflectance or emission of each component of the given mixture as a function of the wavelength or frequency of the illuminating or scattered light transmitted through the mixture. This, of course, requires that the mixture be separable into its component parts. Such measurements as a function of wavelength or frequency produce a plot that is generally referred to as a spectrum. The spectra of the components of a given mixture, material or object, i.e., a sample spectra, can be identified by comparing the sample spectra to a set of reference spectra that have been individually collected for a set of known elements or materials. The set of reference spectra are typically referred to as a spectral library, and the process of comparing the sample spectra to the spectral library is generally termed a spectral library search. Spectral library searches have been described in the literature for many years, and are widely used today. Spectral library searches using infrared (approximately 750 nm to 1000 μm wavelength), Raman, fluorescence or near infrared (approximately 750 nm to 2500 nm wavelength) transmissions are well suited to identify many materials due to the rich set of detailed features these spectroscopy techniques generally produce. The above-identified spectroscopy techniques provide a rich fingerprint of the various pure entities that are currently used to identify them in mixtures which are separable into its component parts via spectral library searching.
While spectral library searching is a widely used method of determining the composition of mixtures, there are a number of factors that can complicate the process of spectral library searching. In an ideal world, the spectrum of a mixture, material, or component part thereof would only contain information that corresponds to the chemical constituency of that mixture, material, or component part. However, in actuality, most spectra also contain information that is related to the instrument response function of the instrument used to collect the spectra. Various known correctional algorithms are typically applied to the raw spectral data in an attempt to minimize the amount of instrumental information contained in both the reference and sample spectra.
Another problem with spectral library searching is that many samples of interest submitted for identification are mixtures rather than pure components. Spectral library searching can only be used to identify pure components. The number of possible mixtures of even a limited multi-component system is very large. The spectrum of a mixture typically differs significantly from the spectra of the pure components that comprise the mixture. Since a typical spectral library stores only spectra of pure components, the current, commercially available spectral library packages are generally unable to identify the components of any given mixture that a user might analyze. Therefore, a method to clearly delineate and identify various materials and, more specifically, toxins and pathogens, when they occur in mixtures is both a timely and important problem that is addressed by the present invention.
Several multivariate statistical techniques are currently available that allow a data analyst to identify components of a particular mixture from their spectra. One such technique is the “target factor testing” approach that has been developed by Malinowski (see E. R. Malinowski, Factor Analysis in Chemistry, Wiley-Interscience, New York, 1991), the disclosure of which is incorporated by reference herein. Target factor testing results in a ranking of the target spectra, i.e., those spectra that are considered as potential candidates of the mixture, and reports the top x targets as the pure components of the mixture. It has been found, however, that there are many cases where the actual components of the mixture are ranked high in the candidate list, but are not ranked within the top x matches (where x is the number of pure components in the mixture). Thus, target factor testing has certain disadvantages when used to identify toxins and biological pathogens in mixtures, since a high degree of reliability and accuracy is generally desired.
The present invention is directed toward overcoming one or more of the above-mentioned problems.